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An Q, Li Y, Sun Z, Gao X, Wang H. Seasonal prediction of the distribution of three major malaria vectors in China: Based on an ecological niche model. PLoS Negl Trop Dis 2024; 18:e0011884. [PMID: 38236812 PMCID: PMC10796015 DOI: 10.1371/journal.pntd.0011884] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Accepted: 12/26/2023] [Indexed: 01/22/2024] Open
Abstract
Against the backdrop of a global malaria epidemic that remains severe, China has eradicated indigenous malaria but still has to be alert to the risk of external importation. Understanding the distribution of vectors can provide an adequate and reliable basis for the development and implementation of vector control strategies. However, with the decline of malaria prevalence in recent years, the capacity of vector monitoring and identification has been greatly weakened. Here we have used new sampling records, climatic data, and topographic data to establish ecological niche models of the three main malaria vectors in China. The model results accurately identified the current habitat suitability areas for the three species of Anopheles and revealed that in addition to precipitation and temperature as important variables affecting the distribution of Anopheles mosquitoes, topographic variables also influenced the distribution of Anopheles mosquitoes. Anopheles sinensis is the most widespread malaria vector in China, with a wide region from the northeast (Heilongjiang Province) to the southwest (Yunnan Province) suitable for its survival. Suitable habitat areas for Anopheles lesteri are concentrated in the central, eastern, and southern regions of China. The suitable habitat areas of Anopheles minimus are the smallest and are only distributed in the border provinces of southern China. On this basis, we further assessed the seasonal variation in habitat suitability areas for these three major malaria vectors in China. The results of this study provide new and more detailed evidence for vector monitoring. In this new era of imported malaria prevention in China, regular reassessment of the risk of vector transmission is recommended.
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Affiliation(s)
- Qi An
- College of Veterinary Medicine, Northeast Agricultural University, Harbin, People’s Republic of China
- Key Laboratory of the Provincial Education Department of Heilongjiang for Common Animal Disease Prevention and Treatment, College of Veterinary Medicine, Northeast Agricultural University, Harbin, People’s Republic of China
| | - Yuepeng Li
- College of Veterinary Medicine, Northeast Agricultural University, Harbin, People’s Republic of China
- Key Laboratory of the Provincial Education Department of Heilongjiang for Common Animal Disease Prevention and Treatment, College of Veterinary Medicine, Northeast Agricultural University, Harbin, People’s Republic of China
| | - Zhuo Sun
- College of Veterinary Medicine, Northeast Agricultural University, Harbin, People’s Republic of China
- Key Laboratory of the Provincial Education Department of Heilongjiang for Common Animal Disease Prevention and Treatment, College of Veterinary Medicine, Northeast Agricultural University, Harbin, People’s Republic of China
| | - Xiang Gao
- College of Veterinary Medicine, Northeast Agricultural University, Harbin, People’s Republic of China
- Key Laboratory of the Provincial Education Department of Heilongjiang for Common Animal Disease Prevention and Treatment, College of Veterinary Medicine, Northeast Agricultural University, Harbin, People’s Republic of China
| | - Hongbin Wang
- College of Veterinary Medicine, Northeast Agricultural University, Harbin, People’s Republic of China
- Key Laboratory of the Provincial Education Department of Heilongjiang for Common Animal Disease Prevention and Treatment, College of Veterinary Medicine, Northeast Agricultural University, Harbin, People’s Republic of China
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Guillot C, Bouchard C, Aenishaenslin C, Berthiaume P, Milord F, Leighton PA. Criteria for selecting sentinel unit locations in a surveillance system for vector-borne disease: A decision tool. Front Public Health 2022; 10:1003949. [PMID: 36438246 PMCID: PMC9686450 DOI: 10.3389/fpubh.2022.1003949] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Accepted: 10/10/2022] [Indexed: 11/11/2022] Open
Abstract
Objectives With vector-borne diseases emerging across the globe, precipitated by climate change and other anthropogenic changes, it is critical for public health authorities to have well-designed surveillance strategies in place. Sentinel surveillance has been proposed as a cost-effective approach to surveillance in this context. However, spatial design of sentinel surveillance system has important impacts on surveillance outcomes, and careful selection of sentinel unit locations is therefore an essential component of planning. Methods A review of the available literature, based on the realist approach, was used to identify key decision issues for sentinel surveillance planning. Outcomes of the review were used to develop a decision tool, which was subsequently validated by experts in the field. Results The resulting decision tool provides a list of criteria which can be used to select sentinel unit locations. We illustrate its application using the case example of designing a national sentinel surveillance system for Lyme disease in Canada. Conclusions The decision tool provides researchers and public health authorities with a systematic, evidence-based approach for planning the spatial design of sentinel surveillance systems, taking into account the aims of the surveillance system and disease and/or context-specific considerations.
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Affiliation(s)
- Camille Guillot
- Groupe de recherche en épidémiologie des zoonoses et santé publique (GREZOSP), Faculté de médecine vétérinaire, Université de Montréal, Saint-Hyacinthe, QC, Canada,Faculté de médecine et des sciences de la santé, Université de Sherbrooke, Sherbrooke, QC, Canada,Centre de recherche en santé publique de l'Université de Montréal et du CIUSSS du Centre-Sud-de-l'île-de-Montréal (CReSP), Montréal, QC, Canada,*Correspondence: Camille Guillot
| | - Catherine Bouchard
- Groupe de recherche en épidémiologie des zoonoses et santé publique (GREZOSP), Faculté de médecine vétérinaire, Université de Montréal, Saint-Hyacinthe, QC, Canada,Public Health Risk Sciences Division, National Microbiology Laboratory, Public Health Agency of Canada, St. Hyacinthe, QC, Canada
| | - Cécile Aenishaenslin
- Groupe de recherche en épidémiologie des zoonoses et santé publique (GREZOSP), Faculté de médecine vétérinaire, Université de Montréal, Saint-Hyacinthe, QC, Canada
| | - Philippe Berthiaume
- Groupe de recherche en épidémiologie des zoonoses et santé publique (GREZOSP), Faculté de médecine vétérinaire, Université de Montréal, Saint-Hyacinthe, QC, Canada,Public Health Risk Sciences Division, National Microbiology Laboratory, Public Health Agency of Canada, St. Hyacinthe, QC, Canada
| | - François Milord
- Faculté de médecine et des sciences de la santé, Université de Sherbrooke, Sherbrooke, QC, Canada
| | - Patrick A. Leighton
- Groupe de recherche en épidémiologie des zoonoses et santé publique (GREZOSP), Faculté de médecine vétérinaire, Université de Montréal, Saint-Hyacinthe, QC, Canada,Centre de recherche en santé publique de l'Université de Montréal et du CIUSSS du Centre-Sud-de-l'île-de-Montréal (CReSP), Montréal, QC, Canada
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Akintola AA, Park B, Choi EH, Hwang UW. Complete mitochondrial genome of a malaria vector mosquito Anopheles sinensis from South Korea. Mitochondrial DNA B Resour 2022; 7:881-883. [PMID: 35692711 PMCID: PMC9176363 DOI: 10.1080/23802359.2022.2077665] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
We present the complete mitochondrial genome of a Malaria vector Mosquito Anopheles sinensis Wiedemann, 1828 from South Korea. The mitochondrial genome is about 15,421 bp long and contains 13 protein-coding genes (PCGs), 22 tRNA genes, two rRNA genes, and an A-T rich region. The present data were compared with those from China with respect to PCG sequence differences, tRNA structure, gene order, and control region (CR) structure. An. sinensis mitochondrial genomes from northeast Asia share identical gene composition and gene order. In contrast, they have distinct differences in the CR within the range 8.75% (51/583 bp) to 9.95% (58/583 bp). The phylogenetic analysis showed that An. sinensis from South Korea was clustered together with those from China, but there existed distinct genetic distance between the two. Likewise, mitochondrial genome sequences from other Anopheles species were employed to infer phylogenetic relationships among the members of the genus Anopheles. This study further promotes the enrichment of An. sinensis mitochondrial genome data, providing useful information for their mitochondrial genetic differences along with geographical distances in northeast Asia.
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Affiliation(s)
| | - Bia Park
- Department of Biology Education, Teachers College & Institute for Phylogenomics and Evolution, Kyungpook National University, Daegu, South Korea
| | - Eun Hwa Choi
- Department of Biology Education, Teachers College & Institute for Phylogenomics and Evolution, Kyungpook National University, Daegu, South Korea
| | - Ui Wook Hwang
- School of Industrial Technology Advances, Kyungpook National University, Daegu, South Korea
- Department of Biology Education, Teachers College & Institute for Phylogenomics and Evolution, Kyungpook National University, Daegu, South Korea
- Institute for Korean Herb-Bio Convergence Promotion, Kyungpook National University, Daegu, South Korea
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Smith JL, Mumbengegwi D, Haindongo E, Cueto C, Roberts KW, Gosling R, Uusiku P, Kleinschmidt I, Bennett A, Sturrock HJ. Malaria risk factors in northern Namibia: The importance of occupation, age and mobility in characterizing high-risk populations. PLoS One 2021; 16:e0252690. [PMID: 34170917 PMCID: PMC8232432 DOI: 10.1371/journal.pone.0252690] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Accepted: 05/20/2021] [Indexed: 11/19/2022] Open
Abstract
In areas of low and unstable transmission, malaria cases occur in populations with lower access to malaria services and interventions, and in groups with specific malaria risk exposures often away from the household. In support of the Namibian National Vector Borne Disease Program's drive to better target interventions based upon risk, we implemented a health facility-based case control study aimed to identify risk factors for symptomatic malaria in Zambezi Region, northern Namibia. A total of 770 febrile individuals reporting to 6 health facilities and testing positive by rapid diagnostic test (RDT) between February 2015 and April 2016 were recruited as cases; 641 febrile individuals testing negative by RDT at the same health facilities through June 2016 were recruited as controls. Data on socio-demographics, housing construction, overnight travel, use of malaria prevention and outdoor behaviors at night were collected through interview and recorded on a tablet-based questionnaire. Remotely-sensed environmental data were extracted for geo-located village residence locations. Multivariable logistic regression was conducted to identify risk factors and latent class analyses (LCA) used to identify and characterize high-risk subgroups. The majority of participants (87% of cases and 69% of controls) were recruited during the 2016 transmission season, an outbreak year in Southern Africa. After adjustment, cases were more likely to be cattle herders (Adjusted Odds Ratio (aOR): 4.46 95%CI 1.05-18.96), members of the police or other security personnel (aOR: 4.60 95%CI: 1.16-18.16), and pensioners/unemployed persons (aOR: 2.25 95%CI 1.24-4.08), compared to agricultural workers (most common category). Children (aOR 2.28 95%CI 1.13-4.59) and self-identified students were at higher risk of malaria (aOR: 4.32 95%CI 2.31-8.10). Other actionable risk factors for malaria included housing and behavioral characteristics, including traditional home construction and sleeping in an open structure (versus modern structure: aOR: 2.01 95%CI 1.45-2.79 and aOR: 4.76 95%CI: 2.14-10.57); cross border travel in the prior 30 days (aOR: 10.55 95%CI 2.94-37.84); and outdoor agricultural work at night (aOR: 2.09 95%CI 1.12-3.87). Malaria preventive activities were all protective and included personal use of an insecticide treated net (ITN) (aOR: 0.61 95%CI 0.42-0.87), adequate household ITN coverage (aOR: 0.63 95%CI 0.42-0.94), and household indoor residual spraying (IRS) in the past year (versus never sprayed: (aOR: 0.63 95%CI 0.44-0.90). A number of environmental factors were associated with increased risk of malaria, including lower temperatures, higher rainfall and increased vegetation for the 30 days prior to diagnosis and residing more than 5 minutes from a health facility. LCA identified six classes of cases, with class membership strongly correlated with occupation, age and select behavioral risk factors. Use of ITNs and IRS coverage was similarly low across classes. For malaria elimination these high-risk groups will need targeted and tailored intervention strategies, for example, by implementing alternative delivery methods of interventions through schools and worksites, as well as the use of specific interventions that address outdoor transmission.
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Affiliation(s)
- Jennifer L. Smith
- Malaria Elimination Initiative, Global Health Group, University of California San Francisco (UCSF), San Francisco, California, United States of America
| | - Davis Mumbengegwi
- Multidisciplinary Research Centre, University of Namibia, Windhoek, Namibia
| | - Erastus Haindongo
- School of Medicine, Faculty of Health Sciences, University of Namibia, Windhoek, Namibia
| | - Carmen Cueto
- Malaria Elimination Initiative, Global Health Group, University of California San Francisco (UCSF), San Francisco, California, United States of America
| | - Kathryn W. Roberts
- Malaria Elimination Initiative, Global Health Group, University of California San Francisco (UCSF), San Francisco, California, United States of America
| | - Roly Gosling
- Malaria Elimination Initiative, Global Health Group, University of California San Francisco (UCSF), San Francisco, California, United States of America
| | - Petrina Uusiku
- National Ministry of Health and Social Services, Windhoek, Namibia
| | - Immo Kleinschmidt
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, United Kingdom
- Department of Immunology and Infection, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Adam Bennett
- Malaria Elimination Initiative, Global Health Group, University of California San Francisco (UCSF), San Francisco, California, United States of America
| | - Hugh J. Sturrock
- Malaria Elimination Initiative, Global Health Group, University of California San Francisco (UCSF), San Francisco, California, United States of America
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Liu Z, Wang S, Zhang Y, Xiang J, Tong MX, Gao Q, Zhang Y, Sun S, Liu Q, Jiang B, Bi P. Effect of temperature and its interactions with relative humidity and rainfall on malaria in a temperate city Suzhou, China. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2021; 28:16830-16842. [PMID: 33394450 DOI: 10.1007/s11356-020-12138-4] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/26/2019] [Accepted: 12/16/2020] [Indexed: 06/12/2023]
Abstract
Malaria is a climate-sensitive infectious disease. Many ecological studies have investigated the independent impacts of ambient temperature on malaria. However, the optimal temperature measures of malaria and its interaction with other meteorological factors on malaria transmission are less understood. This study aims to investigate the effect of ambient temperature and its interactions with relative humidity and rainfall on malaria in Suzhou, a temperate climate city in Anhui Province, China. Weekly malaria and meteorological data from 2005 to 2012 were obtained for Suzhou. A distributed lag nonlinear model was conducted to quantify the effect of different temperature measures on malaria. The best measure was defined as that with the minimum quasi-Akaike information criterion. GeoDetector and Poisson regression models were employed to quantify the interactions of temperature, relative humidity, and rainfall on malaria transmission. A total of 13,382 malaria cases were notified in Suzhou from 2005 to 2012. Each 5 °C rise in average temperature over 10 °C resulted in a 22% (95% CI: 17%, 28%) increase in malaria cases at lag of 4 weeks. In terms of cumulative effects from lag 1 to 8 weeks, each 5 °C increase over 10 °C caused a 175% growth in malaria cases (95% CI: 139%, 216%). Average temperature achieved the best performance in terms of model fitting, followed by minimum temperature, most frequent temperature, and maximum temperature. Temperature had an interactive effect on malaria with relative humidity and rainfall. High temperature together with high relative humidity and high rainfall could accelerate the transmission of malaria. Meteorological factors may affect malaria transmission interactively. The research findings could be helpful in the development of weather-based malaria early warning system, especially in the context of climate change for the prevention of possible malaria resurgence.
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Affiliation(s)
- Zhidong Liu
- Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan City, Shandong Province, People's Republic of China
- Shandong University Climate Change and Health Center, Jinan City, Shandong Province, People's Republic of China
| | - Shuzi Wang
- Shandong University Climate Change and Health Center, Jinan City, Shandong Province, People's Republic of China
- Department of Epidemiology, School of Public Health, Cheeloo College of Medicine, Shandong University, No. 44 Wenhuaxi Road, Jinan City, 250012, Shandong Province, People's Republic of China
| | - Ying Zhang
- School of Public Health, China Studies Centre, The University of Sydney, Sydney, New South Wales, Australia
| | - Jianjun Xiang
- School of Public Health, Fujian Medical University, Fuzhou, People's Republic of China
- School of Public Health, The University of Adelaide, Adelaide, South Australia, Australia
| | - Michael Xiaoliang Tong
- School of Public Health, The University of Adelaide, Adelaide, South Australia, Australia
| | - Qi Gao
- Shandong University Climate Change and Health Center, Jinan City, Shandong Province, People's Republic of China
- Department of Epidemiology, School of Public Health, Cheeloo College of Medicine, Shandong University, No. 44 Wenhuaxi Road, Jinan City, 250012, Shandong Province, People's Republic of China
| | - Yiwen Zhang
- Shandong University Climate Change and Health Center, Jinan City, Shandong Province, People's Republic of China
- Department of Epidemiology, School of Public Health, Cheeloo College of Medicine, Shandong University, No. 44 Wenhuaxi Road, Jinan City, 250012, Shandong Province, People's Republic of China
| | - Shuyue Sun
- National Meteorological Center, China Meteorological Administration, Beijing, People's Republic of China
| | - Qiyong Liu
- Shandong University Climate Change and Health Center, Jinan City, Shandong Province, People's Republic of China
- State Key Laboratory of Infectious Disease Prevention and Control, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, 102206, People's Republic of China
| | - Baofa Jiang
- Shandong University Climate Change and Health Center, Jinan City, Shandong Province, People's Republic of China.
- Department of Epidemiology, School of Public Health, Cheeloo College of Medicine, Shandong University, No. 44 Wenhuaxi Road, Jinan City, 250012, Shandong Province, People's Republic of China.
| | - Peng Bi
- School of Public Health, The University of Adelaide, Adelaide, South Australia, Australia
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Zheng J, Shi B, Xia S, Yang G, Zhou XN. Spatial patterns of <em>Plasmodium vivax</em> transmission explored by multivariate auto-regressive state-space modelling - A case study in Baoshan Prefecture in southern China. GEOSPATIAL HEALTH 2021; 16. [PMID: 33733649 DOI: 10.4081/gh.2021.879] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/09/2020] [Accepted: 08/21/2020] [Indexed: 06/12/2023]
Abstract
The transition from the control phase to elimination of malaria in China through the national malaria elimination programme has focussed attention on the need for improvement of the surveillance- response systems. It is now understood that routine passive surveillance is inadequate in the parasite elimination phase that requires supplementation by active surveillance in foci where cluster cases have occurred. This study aims to explore the spatial clusters and temporal trends of malaria cases by the multivariate auto-regressive state-space model (MARSS) along the border to Myanmar in southern China. Data for indigenous cases spanning the period from 2007 to 2010 were extracted from the China's Infectious Diseases Information Reporting Management System (IDIRMS). The best MARSS model indicated that malaria transmission in the study area during 36 months could be grouped into three clusters. The estimation of malaria transmission patterns showed a downward trend across all clusters. The proposed methodology used in this study offers a simple and rapid, yet effective way to categorize patterns of foci which provide assistance for active monitoring of malaria in the elimination phase.
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Affiliation(s)
- Jinxin Zheng
- National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention, Shanghai, China; Key Laboratory of Parasite and Vector Biology, National Health Commission, Shanghai, China; National Center for International Research on Tropical Diseases, Ministry of Science and Technology, Shanghai, China; Chinese Center for Tropical Diseases Research, Shanghai.
| | - Benyun Shi
- School of Computer Science and Technology, Nanjing Tech University, Nanjing, Jiangsu.
| | - Shang Xia
- National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention, Shanghai, China; Key Laboratory of Parasite and Vector Biology, National Health Commission, Shanghai, China; National Center for International Research on Tropical Diseases, Ministry of Science and Technology, Shanghai, China; Chinese Center for Tropical Diseases Research, Shanghai.
| | - Guojing Yang
- Hainan Medical University, Laboratory of Tropical Environment and Health, Haikou, Hainan, China; Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute; University of Basel, Basel.
| | - Xiao-Nong Zhou
- National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention, Shanghai, China; Key Laboratory of Parasite and Vector Biology, National Health Commission, Shanghai, China; National Center for International Research on Tropical Diseases, Ministry of Science and Technology, Shanghai, China; Chinese Center for Tropical Diseases Research, Shanghai.
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Wang J, Xu H, Song S, Cheng R, Fan N, Fu S, Zhang S, Xu Z, He Y, Lei W, Li F, Wang H, Lu X, Liang G. Emergence of Zika Virus in Culex tritaeniorhynchus and Anopheles sinensis Mosquitoes in China. Virol Sin 2021; 36:33-42. [PMID: 32617898 PMCID: PMC7973324 DOI: 10.1007/s12250-020-00239-w] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2019] [Accepted: 04/08/2020] [Indexed: 11/30/2022] Open
Abstract
Zika virus (ZIKV) has been isolated from mosquitoes such as Aedes, Mansonia uniformis, and Culex perfuscus; However, the isolation of ZIKV from Anopheles sinensis and Culex tritaeniorhynchus has not yet been reported. In June and July 2018, 22,985 mosquitoes and 57,500 midges were collected in Jiangxi Province in southeastern China. Among them, six strains of ZIKV were isolated from mosquitoes: four from An. sinensis and two from Cx. tritaeniorhynchus. Molecular genetic analysis showed that the ZIKV isolated from An. sinensis and Cx. tritaeniorhynchus belonged to genotype 2 in the Asian evolutionary branch of ZIKV. In addition, the ZIKV strains isolated from An. sinensis and Cx. tritaeniorhynchus had amino acid substitutions identical to ZIKV strains prevalent in South America since 2015. This study is the first to isolate ZIKV from mosquito specimens collected in the wild of Jiangxi Province, China; This is also the first time that ZIKV has been isolated from An. sinensis and Cx. tritaeniorhynchus. Given that An. sinensis and Cx. tritaeniorhynchus have a very wide geographical distribution in China and even in eastern and southern Asia, the isolation of several strains of ZIKV from these two mosquitoes poses new challenges for the prevention and control of ZIKV infection in the mainland of China and countries and regions with the same distribution of mosquitoes.
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Affiliation(s)
- Jing Wang
- Qingdao University, Qingdao, 266071, China
- State Key Laboratory of Infectious Disease Prevention and Control, National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, 102206, China
| | - Hongbin Xu
- Jiangxi Province Center for Disease Control and Prevention, Nanchang, 330029, China
| | - Song Song
- Qingdao Municipal Center for Disease Control and Prevention, Qingdao, 266071, China
| | - Rui Cheng
- Qingdao University, Qingdao, 266071, China
- State Key Laboratory of Infectious Disease Prevention and Control, National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, 102206, China
| | - Na Fan
- Qingdao University, Qingdao, 266071, China
- State Key Laboratory of Infectious Disease Prevention and Control, National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, 102206, China
| | - Shihong Fu
- State Key Laboratory of Infectious Disease Prevention and Control, National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, 102206, China
| | - Shaozai Zhang
- Jiangxi Province Center for Disease Control and Prevention, Nanchang, 330029, China
| | - Ziqian Xu
- State Key Laboratory of Infectious Disease Prevention and Control, National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, 102206, China
| | - Ying He
- State Key Laboratory of Infectious Disease Prevention and Control, National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, 102206, China
| | - Wenwen Lei
- State Key Laboratory of Infectious Disease Prevention and Control, National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, 102206, China
| | - Fan Li
- State Key Laboratory of Infectious Disease Prevention and Control, National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, 102206, China
| | - Huanyu Wang
- State Key Laboratory of Infectious Disease Prevention and Control, National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, 102206, China.
| | | | - Guodong Liang
- State Key Laboratory of Infectious Disease Prevention and Control, National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, 102206, China.
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8
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Kim S, Kim Y. Spatially Filtered Multilevel Analysis on Spatial Determinants for Malaria Occurrence in Korea. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2019; 16:ijerph16071250. [PMID: 30965608 PMCID: PMC6480462 DOI: 10.3390/ijerph16071250] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/10/2019] [Revised: 03/26/2019] [Accepted: 03/30/2019] [Indexed: 11/16/2022]
Abstract
Since its re-emergence in 1993, the spatial patterns of malaria outbreaks in South Korea have drastically changed. It is well known that complicated interactions between humans, nature, and socio-economic factors lead to a spatial dependency of vivax malaria occurrences. This study investigates the spatial factors determining malaria occurrences in order to understand and control malaria risks in Korea. A multilevel model is applied to simultaneously analyze the variables in different spatial scales, and eigenvector spatial filtering is used to explain the spatial autocorrelation in the malaria occurrence data. The results show that housing costs, average age, rice paddy field ratio, and distance from the demilitarized zone (DMZ) are significant on the level-1 spatial scale; health budget per capita and military base area ratio are significant on the level-2 spatial scale. The results show that the spatially filtered multilevel model provides better analysis results in handling spatial issues.
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Affiliation(s)
- Sehyeong Kim
- Department of Geography, Korea University, 145 Anam-ro, Seoul 02841, Korea.
| | - Youngho Kim
- Department of Geography Education, Korea University, 145 Anam-ro, Seoul 02841, Korea.
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9
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Lai S, Sun J, Ruktanonchai NW, Zhou S, Yu J, Routledge I, Wang L, Zheng Y, Tatem AJ, Li Z. Changing epidemiology and challenges of malaria in China towards elimination. Malar J 2019; 18:107. [PMID: 30922301 PMCID: PMC6440015 DOI: 10.1186/s12936-019-2736-8] [Citation(s) in RCA: 50] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2018] [Accepted: 03/18/2019] [Indexed: 12/24/2022] Open
Abstract
Background Historically, malaria had been a widespread disease in China. A national plan was launched in China in 2010, aiming to eliminate malaria by 2020. In 2017, no indigenous cases of malaria were detected in China for the first time. To provide evidence for precise surveillance and response to achieve elimination goal, a comprehensive study is needed to determine the changing epidemiology of malaria and the challenges towards elimination. Methods Using malaria surveillance data from 2011 to 2016, an integrated series of analyses was conducted to elucidate the changing epidemiological features of autochthonous and imported malaria, and the spatiotemporal patterns of malaria importation from endemic countries. Results From 2011 to 2016, a total of 21,062 malaria cases with 138 deaths were reported, including 91% were imported and 9% were autochthonous. The geographic distribution of local transmission have shrunk dramatically, but there were still more than 10 counties reporting autochthonous cases in 2013–2016, particularly in counties bordering with countries in South-East Asia. The importation from 68 origins countries had an increasing annual trend from Africa but decreasing importation from Southeast Asia. Four distinct communities have been identified in the importation networks with the destinations in China varied by origin and species. Conclusions China is on the verge of malaria elimination, but the residual transmission in border regions and the threats of importation from Africa and Southeast Asia are the key challenges to achieve and maintain malaria elimination. Efforts from China are also needed to help malaria control in origin countries and reduce the risk of introduced transmission. Electronic supplementary material The online version of this article (10.1186/s12936-019-2736-8) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Shengjie Lai
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, UK.,Division of Infectious Disease, Key Laboratory of Surveillance and Early-Warning on Infectious Disease, Chinese Center for Disease Control and Prevention, Beijing, China.,School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Junling Sun
- Division of Infectious Disease, Key Laboratory of Surveillance and Early-Warning on Infectious Disease, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Nick W Ruktanonchai
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, UK.,Flowminder Foundation, Stockholm, Sweden
| | - Sheng Zhou
- Division of Infectious Disease, Key Laboratory of Surveillance and Early-Warning on Infectious Disease, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Jianxing Yu
- Division of Infectious Disease, Key Laboratory of Surveillance and Early-Warning on Infectious Disease, Chinese Center for Disease Control and Prevention, Beijing, China.,MOH Key Laboratory of Systems Biology of Pathogens and Christophe Mérieux Laboratory, CAMS-Fondation Mérieux, Institute of Pathogen Biology, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Isobel Routledge
- Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Liping Wang
- Division of Infectious Disease, Key Laboratory of Surveillance and Early-Warning on Infectious Disease, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Yaming Zheng
- Division of Infectious Disease, Key Laboratory of Surveillance and Early-Warning on Infectious Disease, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Andrew J Tatem
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, UK.,Flowminder Foundation, Stockholm, Sweden
| | - Zhongjie Li
- Division of Infectious Disease, Key Laboratory of Surveillance and Early-Warning on Infectious Disease, Chinese Center for Disease Control and Prevention, Beijing, China.
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10
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Chen K, Wang Y, Li XY, Peng H, Ma YJ. Sequencing and analysis of the complete mitochondrial genome in Anopheles sinensis (Diptera: Culicidae). Infect Dis Poverty 2017; 6:149. [PMID: 28969698 PMCID: PMC5625653 DOI: 10.1186/s40249-017-0362-7] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2017] [Accepted: 09/16/2017] [Indexed: 11/12/2022] Open
Abstract
Background Anopheles sinensis (Diptera: Culicidae) is a primary vector of Plasmodium vivax and Brugia malayi in most regions of China. In addition, its phylogenetic relationship with the cryptic species of the Hyrcanus Group is complex and remains unresolved. Mitochondrial genome sequences are widely used as molecular markers for phylogenetic studies of mosquito species complexes, of which mitochondrial genome data of An. sinensis is not available. Methods An. sinensis samples was collected from Shandong, China, and identified by molecular marker. Genomic DNA was extracted, followed by the Illumina sequencing. Two complete mitochondrial genomes were assembled and annotated using the mitochondrial genome of An. gambiae as reference. The mitochondrial genomes sequences of the 28 known Anopheles species were aligned and reconstructed phylogenetic tree by Maximum Likelihood (ML) method. Findings The length of complete mitochondrial genomes of An. sinensis was 15,076 bp and 15,138 bp, consisting of 13 protein-coding genes, 22 transfer RNA (tRNA) genes, 2 ribosomal RNA (rRNA) genes, and an AT-rich control region. As in other insects, most mitochondrial genes are encoded on the J strand, except for ND5, ND4, ND4L, ND1, two rRNA and eight tRNA genes, which are encoded on the N strand. The bootstrap value was set as 1000 in ML analyses. The topologies restored phylogenetic affinity within subfamily Anophelinae. The ML tree showed four major clades, corresponding to the subgenera Cellia, Anopheles, Nyssorhynchus and Kerteszia of the genus Anopheles. Conclusions The complete mitochondrial genomes of An. sinensis were obtained. The number, order and transcription direction of An. sinensis mitochondrial genes were the same as in other species of family Culicidae. Electronic supplementary material The online version of this article (10.1186/s40249-017-0362-7) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Kai Chen
- Department of Tropical Infectious Diseases, Second Military Medical University, Shanghai, 200433, China.,Team ten Cadet Brigade, Second Military Medical University, Shanghai, 200433, China
| | - Yan Wang
- Department of Tropical Infectious Diseases, Second Military Medical University, Shanghai, 200433, China
| | - Xiang-Yu Li
- Department of Medical Microbiology and Parasitology, Second Military Medical University, Shanghai, 200433, China
| | - Heng Peng
- Department of Medical Microbiology and Parasitology, Second Military Medical University, Shanghai, 200433, China.
| | - Ya-Jun Ma
- Department of Tropical Infectious Diseases, Second Military Medical University, Shanghai, 200433, China.
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11
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Zhang S, Guo S, Feng X, Afelt A, Frutos R, Zhou S, Manguin S. Anopheles Vectors in Mainland China While Approaching Malaria Elimination. Trends Parasitol 2017; 33:889-900. [PMID: 28734898 DOI: 10.1016/j.pt.2017.06.010] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2017] [Revised: 06/22/2017] [Accepted: 06/29/2017] [Indexed: 01/27/2023]
Abstract
China is approaching malaria elimination; however, well-documented information on malaria vectors is still missing, which could hinder the development of appropriate surveillance strategies and WHO certification. This review summarizes the nationwide distribution of malaria vectors, their bionomic characteristics, control measures, and related studies. After several years of effort, the area of distribution of the principal malaria vectors was reduced, in particular for Anopheles lesteri (synonym: An. anthropophagus) and Anopheles dirus s.l., which nearly disappeared from their former endemic regions. Anopheles sinensis is becoming the predominant species in southwestern China. The bionomic characteristics of these species have changed, and resistance to insecticides was reported. There is a need to update surveillance tools and investigate the role of secondary vectors in malaria transmission.
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Affiliation(s)
- Shaosen Zhang
- National Institute of Parasitic Diseases, Chinese Centre for Disease Control and Prevention; Key Laboratory of Parasite and Vector Biology, MOH; WHO Collaborating Centre for Tropical Diseases, National Centre for International Research on Tropical Diseases, Shanghai, China; Université de Montpellier, IES-Institut d'Electronique et des Systèmes, UMR5214, CNRS-UM, 860 rue de Saint-Priest, Bât 5, 34095 Montpellier, France; Cirad, UMR 17, Intertryp, Campus international de Baillarguet, 34398 Montpellier, Cedex 5, France; Institut de Recherche pour le Développement (IRD France), LIPMC, UMR-MD3, Faculté de Pharmacie, 34093 Montpellier, France
| | - Shaohua Guo
- National Institute of Parasitic Diseases, Chinese Centre for Disease Control and Prevention; Key Laboratory of Parasite and Vector Biology, MOH; WHO Collaborating Centre for Tropical Diseases, National Centre for International Research on Tropical Diseases, Shanghai, China; Jiading District Center for Disease Control and Prevention, Shanghai, China
| | - Xinyu Feng
- National Institute of Parasitic Diseases, Chinese Centre for Disease Control and Prevention; Key Laboratory of Parasite and Vector Biology, MOH; WHO Collaborating Centre for Tropical Diseases, National Centre for International Research on Tropical Diseases, Shanghai, China
| | - Aneta Afelt
- Interdisciplinary Center for Mathematical and Computational Modelling, University of Warsaw, Prosta 69, 00-838, Warsaw, Poland
| | - Roger Frutos
- Université de Montpellier, IES-Institut d'Electronique et des Systèmes, UMR5214, CNRS-UM, 860 rue de Saint-Priest, Bât 5, 34095 Montpellier, France; Cirad, UMR 17, Intertryp, Campus international de Baillarguet, 34398 Montpellier, Cedex 5, France
| | - Shuisen Zhou
- National Institute of Parasitic Diseases, Chinese Centre for Disease Control and Prevention; Key Laboratory of Parasite and Vector Biology, MOH; WHO Collaborating Centre for Tropical Diseases, National Centre for International Research on Tropical Diseases, Shanghai, China.
| | - Sylvie Manguin
- Institut de Recherche pour le Développement (IRD France), LIPMC, UMR-MD3, Faculté de Pharmacie, 34093 Montpellier, France
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12
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Feng X, Huang L, Lin L, Yang M, Ma Y. Genetic diversity and population structure of the primary malaria vector Anopheles sinensis (Diptera: Culicidae) in China inferred by cox1 gene. Parasit Vectors 2017; 10:75. [PMID: 28183358 PMCID: PMC5439230 DOI: 10.1186/s13071-017-2013-z] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2016] [Accepted: 02/04/2017] [Indexed: 12/22/2022] Open
Abstract
Background Anopheles sinensis is a primary vector for Plasmodium vivax malaria in most regions of China. A comprehensive understanding of genetic variation and structure of the mosquito would be of benefit to the vector control and in a further attempt to contribute to malaria elimination in China. However, there is only inadequate population genetic data pertaining to An. sinensis currently. Methods Genetic variations and structure among populations of An. sinensis was examined and analyzed based on the nucleotide sequences of a 662 nt variable region of the mitochondrial cox1 gene among 15 populations from 20 collection sites in China. Results A total of 453 individuals in 15 populations were analyzed. The cox1 gene sequences were aligned, and 247 haplotypes were detected, 41 of these shared between populations. The range of haplotype diversity was from 0.709 (Yunnan) to 0.998 (Anhui). The genealogic network showed that the haplotypes were divided into two clusters, cluster I was at a high level of homoplasy, while cluster II included almost all individuals from the Yunnan population. The Yunnan population displayed a significantly high level of genetic differentiation (0.452−0.622) and a restricted gene flow with other populations. The pairwise FST values among other populations were lower. The AMOVA result showed that the percentage of variation within populations (83.83%) was higher than that among populations (16.17%). Mantel test suggested that geographical distance did not significantly contribute to the genetic differentiation (R2 = 0.0125, P = 0.59). Neutral test and mismatch analysis results showed that the An. sinensis population has undergone demographic expansions. Conclusions Anopheles sinensis populations showed high genetic polymorphism by cox1 gene. The weak genetic structure may be a consequence of low genetic differentiation and high gene flow among populations, except the Yunnan samples. The Yunnan population was isolated from the other populations, gene flow limited by geographical distance and barriers. These findings will provide a theoretical basis for vector surveillance and vector control in China. Electronic supplementary material The online version of this article (doi:10.1186/s13071-017-2013-z) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Xinyu Feng
- WHO Collaborating Center for Tropical Diseases, Key Laboratory of Parasite and Vector Biology, Ministry of Public Health, National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention, Shanghai, 200025, China.,Department of Tropical Infectious Disease, Second Military Medical University, Shanghai, 200433, China
| | - Libin Huang
- Second Military Medical University Press, Shanghai, 200433, China
| | - Lin Lin
- Department of Tropical Infectious Disease, Second Military Medical University, Shanghai, 200433, China
| | - Manni Yang
- Department of Tropical Infectious Disease, Second Military Medical University, Shanghai, 200433, China
| | - Yajun Ma
- Department of Tropical Infectious Disease, Second Military Medical University, Shanghai, 200433, China.
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13
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Smith JL, Auala J, Haindongo E, Uusiku P, Gosling R, Kleinschmidt I, Mumbengegwi D, Sturrock HJW. Malaria risk in young male travellers but local transmission persists: a case-control study in low transmission Namibia. Malar J 2017; 16:70. [PMID: 28187770 PMCID: PMC5303241 DOI: 10.1186/s12936-017-1719-x] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2016] [Accepted: 02/03/2017] [Indexed: 11/24/2022] Open
Abstract
Background A key component of malaria elimination campaigns is the identification and targeting of high risk populations. To characterize high risk populations in north central Namibia, a prospective health facility-based case–control study was conducted from December 2012–July 2014. Cases (n = 107) were all patients presenting to any of the 46 health clinics located in the study districts with a confirmed Plasmodium infection by multi-species rapid diagnostic test (RDT). Population controls (n = 679) for each district were RDT negative individuals residing within a household that was randomly selected from a census listing using a two-stage sampling procedure. Demographic, travel, socio-economic, behavioural, climate and vegetation data were also collected. Spatial patterns of malaria risk were analysed. Multivariate logistic regression was used to identify risk factors for malaria. Results Malaria risk was observed to cluster along the border with Angola, and travel patterns among cases were comparatively restricted to northern Namibia and Angola. Travel to Angola was associated with excessive risk of malaria in males (OR 43.58 95% CI 2.12–896), but there was no corresponding risk associated with travel by females. This is the first study to reveal that gender can modify the effect of travel on risk of malaria. Amongst non-travellers, male gender was also associated with a higher risk of malaria compared with females (OR 1.95 95% CI 1.25–3.04). Other strong risk factors were sleeping away from the household the previous night, lower socioeconomic status, living in an area with moderate vegetation around their house, experiencing moderate rainfall in the month prior to diagnosis and living <15 km from the Angolan border. Conclusions These findings highlight the critical need to target malaria interventions to young male travellers, who have a disproportionate risk of malaria in northern Namibia, to coordinate cross-border regional malaria prevention initiatives and to scale up coverage of prevention measures such as indoor residual spraying and long-lasting insecticide nets in high risk areas if malaria elimination is to be realized. Electronic supplementary material The online version of this article (doi:10.1186/s12936-017-1719-x) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Jennifer L Smith
- Malaria Elimination Initiative, Global Health Group, University of California, San Francisco, CA, USA.
| | - Joyce Auala
- Multidisciplinary Research Center, University of Namibia, Windhoek, Namibia
| | - Erastus Haindongo
- Multidisciplinary Research Center, University of Namibia, Windhoek, Namibia
| | - Petrina Uusiku
- National Vector-Borne Disease Control Programme, Ministry of Health and Social Services, Windhoek, Namibia
| | - Roly Gosling
- Malaria Elimination Initiative, Global Health Group, University of California, San Francisco, CA, USA
| | - Immo Kleinschmidt
- MRC Tropical Epidemiology Group, Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, UK
| | - Davis Mumbengegwi
- Multidisciplinary Research Center, University of Namibia, Windhoek, Namibia
| | - Hugh J W Sturrock
- Malaria Elimination Initiative, Global Health Group, University of California, San Francisco, CA, USA
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14
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Wei X, Yan G, Zhou G, Zhong D, Fang Q, Yang X, Hu D, Chang X. A neural network prediction of environmental determinants of Anopheles sinensis knockdown resistance mutation to pyrethroids in China. JOURNAL OF VECTOR ECOLOGY : JOURNAL OF THE SOCIETY FOR VECTOR ECOLOGY 2016; 41:295-302. [PMID: 27860007 DOI: 10.1111/jvec.12226] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/26/2016] [Accepted: 09/20/2016] [Indexed: 06/06/2023]
Abstract
Selection pressure caused by long-term intensive use of insecticides is the key driving force in resistance development. Additional parameters such as environmental conditions may affect both the mosquito response to insecticides and the selection of resistance mechanisms. In this context, we analyzed the environmental determinants of kdr prevalence in Anopheles sinensis across China. We collected kdr frequency from 48 sites across central and southern China, together with key environmental factors including long-term climatic data, topographic features, main crops, and land cover types. Trend surface analysis found that the distribution of kdr frequency can be partitioned into three regions, namely central China (kdr frequency >80%), western China (kdr frequency varies from 0% to 60%), and southern China (kdr frequency <10%). Seven predictor variables were selected based on a radial basis function neural network model. A multilayer perceptron (MLP) network model revealed that the number of crops in a year was the most important predictor for the kdr mutation rate. Topography, long-term mean climate and land cover all contributed to the kdr mutation rate. The observed mean kdr frequency was 53.0% and the MLP network model-predicted mean was 52.6%, a 0.1% relative error. Predicted kdr frequencies closely matched the observed values. The model explained 92% of the total variance in kdr frequency. The results indicated that kdr was associated with the intensity of pesticide usage. Crop cultivation information, together with environmental factors, may well predict the spatial heterogeneity of kdr mutations in An. sinensis in China.
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Affiliation(s)
- Xing Wei
- Institute of Information Security and Big Data, Central South University, Changsha 410083, Hunan, China
- Department of Microbiology and Parasitology, Anhui Key Laboratory of Infection and Immunity, Bengbu Medical College, Bengbu 233000, Anhui, China
| | - Guiyun Yan
- Program in Public Health, College of Health Sciences, University of California at Irvine, Irvine, California 92697, U.S.A
| | - Guofa Zhou
- Program in Public Health, College of Health Sciences, University of California at Irvine, Irvine, California 92697, U.S.A
| | - Daibin Zhong
- Program in Public Health, College of Health Sciences, University of California at Irvine, Irvine, California 92697, U.S.A
| | - Qiang Fang
- Department of Microbiology and Parasitology, Anhui Key Laboratory of Infection and Immunity, Bengbu Medical College, Bengbu 233000, Anhui, China
| | - Xiaodi Yang
- Department of Microbiology and Parasitology, Anhui Key Laboratory of Infection and Immunity, Bengbu Medical College, Bengbu 233000, Anhui, China
| | - Dehua Hu
- Institute of Information Security and Big Data, Central South University, Changsha 410083, Hunan, China
| | - Xuelian Chang
- Department of Microbiology and Parasitology, Anhui Key Laboratory of Infection and Immunity, Bengbu Medical College, Bengbu 233000, Anhui, China
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15
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Ren Z, Wang D, Ma A, Hwang J, Bennett A, Sturrock HJW, Fan J, Zhang W, Yang D, Feng X, Xia Z, Zhou XN, Wang J. Predicting malaria vector distribution under climate change scenarios in China: Challenges for malaria elimination. Sci Rep 2016; 6:20604. [PMID: 26868185 PMCID: PMC4751525 DOI: 10.1038/srep20604] [Citation(s) in RCA: 53] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2014] [Accepted: 01/08/2016] [Indexed: 01/19/2023] Open
Abstract
Projecting the distribution of malaria vectors under climate change is essential for planning integrated vector control activities for sustaining elimination and preventing reintroduction of malaria. In China, however, little knowledge exists on the possible effects of climate change on malaria vectors. Here we assess the potential impact of climate change on four dominant malaria vectors (An. dirus, An. minimus, An. lesteri and An. sinensis) using species distribution models for two future decades: the 2030 s and the 2050 s. Simulation-based estimates suggest that the environmentally suitable area (ESA) for An. dirus and An. minimus would increase by an average of 49% and 16%, respectively, under all three scenarios for the 2030 s, but decrease by 11% and 16%, respectively in the 2050 s. By contrast, an increase of 36% and 11%, respectively, in ESA of An. lesteri and An. sinensis, was estimated under medium stabilizing (RCP4.5) and very heavy (RCP8.5) emission scenarios. in the 2050 s. In total, we predict a substantial net increase in the population exposed to the four dominant malaria vectors in the decades of the 2030 s and 2050 s, considering land use changes and urbanization simultaneously. Strategies to achieve and sustain malaria elimination in China will need to account for these potential changes in vector distributions and receptivity.
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Affiliation(s)
- Zhoupeng Ren
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Science and Natural Resource Research, Chinese Academy of Sciences, Beijing 100101, China.,University of Chinese Academy of Sciences, Beijing 100049, China.,Key Laboratory of Surveillance and Early Warning on Infectious Disease, Chinese Center for Disease Control and Prevention, Beijing 102206, China
| | - Duoquan Wang
- National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention, Shanghai, China.,World Health Organization Collaborating Centre for Tropical Diseases, Shanghai, China.,National Center for International Research on Tropical Diseases, Shanghai, China
| | - Aimin Ma
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Science and Natural Resource Research, Chinese Academy of Sciences, Beijing 100101, China.,College of Geoscience and Surveying Engineering, China University of Mining and Technology, Beijing 100083, China
| | - Jimee Hwang
- Global Health Group, University of California, San Francisco, San Francisco, California, United States of America.,President's Malaria Initiative, Malaria Branch, Centers for Disease Control and Prevention, Atlanta, United States of America
| | - Adam Bennett
- Global Health Group, University of California, San Francisco, San Francisco, California, United States of America
| | - Hugh J W Sturrock
- Global Health Group, University of California, San Francisco, San Francisco, California, United States of America
| | - Junfu Fan
- School of Civil and Architectural Engineering, Shandong University of Technology, Zibo, China
| | - Wenjie Zhang
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Science and Natural Resource Research, Chinese Academy of Sciences, Beijing 100101, China.,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Dian Yang
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Science and Natural Resource Research, Chinese Academy of Sciences, Beijing 100101, China
| | - Xinyu Feng
- National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention, Shanghai, China.,World Health Organization Collaborating Centre for Tropical Diseases, Shanghai, China.,National Center for International Research on Tropical Diseases, Shanghai, China
| | - Zhigui Xia
- National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention, Shanghai, China.,World Health Organization Collaborating Centre for Tropical Diseases, Shanghai, China.,National Center for International Research on Tropical Diseases, Shanghai, China
| | - Xiao-Nong Zhou
- National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention, Shanghai, China.,World Health Organization Collaborating Centre for Tropical Diseases, Shanghai, China.,National Center for International Research on Tropical Diseases, Shanghai, China
| | - Jinfeng Wang
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Science and Natural Resource Research, Chinese Academy of Sciences, Beijing 100101, China.,Key Laboratory of Surveillance and Early Warning on Infectious Disease, Chinese Center for Disease Control and Prevention, Beijing 102206, China.,Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing, 210023, China
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